A Look at World Mental Health and a Sobering Connection to the American Fentanyl Crisis¶

Welcome to an insightful journey through the realm of mental health data analysis. In this captivating notebook, we embark on an exploration of data science techniques to uncover the intricate tapestry of mental health trends across the globe. The datasets at our disposal, sourced from Kaggle, hold the key to unraveling patterns that extend beyond mere numbers—they paint a vivid picture of our collective mental well-being. Amidst this exploration, we'll unravel a connection that extends beyond statistics and touches the heart of a global challenge. Brace yourself to witness the profound interplay between world mental health and a crisis that has sent shockwaves across American borders—the fentanyl crisis. As we dive into the depths of data, intriguing correlations between mental health prevalence and historical events will come to light.

TLDR: Brace for a data-driven journey through world mental health data, an exploration of Mexico's tumultuous drug wars, and a glimpse into how we'll forecast mental health disorders in the upcoming sections of this notebook.

Intriguingly, within the words of witnesses and amidst the chaos of cartels, we discern the ominous tale of the American fentanyl crisis. Witnesses reveal the audacious smuggling of lethal fentanyl across borders, facilitated by Unmanned Aircraft Systems (drones) and an administration's historic border crisis. As billions flow into the coffers of cartels, dedicated Border Patrol agents find themselves navigating an uncharted battleground.

But this is only the prologue. Our narrative is woven not only with shocking revelations but also with data that speaks to the human experience. Here, pandas and matplotlib come alive, revealing the intricate prevalence trends of mental health disorders. Questions will arise, and we shall explore them: What shifts in prevalence rates speak of our collective well-being? How does the United States' data resonate with the trauma of Mexico's drug wars?

As we dissect each data point, we unveil not only numbers but stories. Stories of resilience, struggle, and hope. We journey through the minds of nations, searching for the threads that connect mental health to global events. From the stability of certain disorders to the stark rise of others, each plot point adds another layer to our tale.

So, dear reader, buckle up. The road ahead winds through data and humanity, revealing the profound entanglement of mental health with world events. We venture into the unknown, armed with insights that will shape the forecast of mental disorders in the chapters yet to come.

!open .

from contextlib import redirect_stdout import io

Importing the data set and a cursory examination¶

We import the necessary libraries (pandas as you may expect) and read in our csv data. Then we print using the head function with its default values (5) and print the columns of the dataframe.

import pandas as pd


df_mental_health = pd.read_csv('prevalence-by-mental-and-substance-use-disorder.csv')

display(df_mental_health.head(2))

# Examine the columns
column_labels = [col[:10] for col in df_mental_health.columns]
print(column_labels)

['Entity', 'Code', 'Year', 'Prevalence', 'Prevalence', 'Prevalence', 'Prevalence', 'Prevalence', 'Prevalence', 'Prevalence']

As you can see the data set consists of Countries, Codes for said countries, years and the prevalence of various mental disorders in said country.

Certainly! Here's a more focused version of the list of questions that highlights the most relevant ones for your article:

Here are some key questions to consider as we explore the dataset:

1. What is the purpose of this dataset? Is it primarily aimed at tracking the prevalence of mental health disorders over time?

2. How do the prevalence rates for different mental health disorders change with age? Are there any age groups more susceptible to certain disorders?

3. Are there any significant differences in the prevalence rates between genders (Sex: Both) for each mental health disorder?

4. What trends can you observe in the prevalence rates over the years? Are there any notable changes or patterns?

5. How might changes in mental health awareness, healthcare policies, or socioeconomic conditions impact the prevalence rates over time?

6. Can you identify any correlations between the prevalence rates of different mental health disorders? Do they tend to increase or decrease together?

7. How might researchers or policymakers use this data to inform mental health interventions and strategies?

8. Are there any limitations to this dataset that should be considered when interpreting the results?

These questions will guide our exploration of the dataset and provide insights into the global mental health landscape. As we navigate through the analysis, feel free to reflect on these questions and contribute to the conversation with your insights and feedback.

Shape¶

Its often useful to get a hold of the shape of a dataframe so that we know how much data we will be working and if we might have to do some "batch processing".

df_mental_health.shape

(6840, 10)

We read 6840 rows and 10 columns. This is managable enough so we won't think about it any more.

Now let's take a look at my home and what's happening when it comes to these disorders.

United States and Answering Some Questions¶

We import matplotlib's pyplot and create a sub dataframe consisting of entries where the country is the United States.

import pandas as pd
import matplotlib.pyplot as plt

# Load your data into a DataFrame
# data = pd.read_csv('your_data.csv')

# Filter data for the United States
usa_data = df_mental_health[df_mental_health['Entity'] == 'United States']

display(usa_data.head())

Now in data processing it is often a good idea to separate the data into python structures. This can help with focusing on some particular features.

Below we create a column of strings. These strings are the disorders/features. We will be able to use this list to look at specific disorders and their relationships to other features including date.

# Get a list of mental health disorder columns
disorder_columns = [
    'Prevalence - Schizophrenia - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Bipolar disorder - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Eating disorders - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Anxiety disorders - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Drug use disorders - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Depressive disorders - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Alcohol use disorders - Sex: Both - Age: Age-standardized (Percent)'
]

NExt we take a key step in understanding the data. We create a scatter plot where on the x-axis we have years and on the y-axis we have the prevalence of the various mental disorders at those years.

# Create a scatter plot with different colors for each disorder
plt.figure(figsize=(8, 6))
for column in disorder_columns:
    plt.scatter(usa_data['Year'], usa_data[column], label=column, alpha=0.7)

plt.xlabel('Year')
plt.ylabel('Prevalence (%)')
plt.title('Prevalence of Mental Health Disorders in the United States')
plt.legend()
plt.grid(True)
plt.show()

As you can see there's a few interesting things to point out. Let's do that now before moving onto a friend and an interesting historical connection hinted at in our data.

Disorders and Their Changes in Prevalences¶

Let's categorize roughly based off of visuals:

stable¶

1. Bipolar
2. Eating

Increasing¶

• Anxiety
• Schizophrenia

Declining¶

*Alcohol Disorders

The fact that these disorders follow distinct trends is interesting perhaps it points to potential underlying mechanisms such as: genetics (stable), environmental (increasing and decreasing).

We will touch more on the increasing aspect in a moment.

Hello Neighbor... Mexico¶

Now I want to point out something that got my interest when I examined Mexico. Being of Mexican descent and being of the personal belief that appreciating one's culture is healthy and enjoyable I decided to look at Mexico's data (isolated).

# Load your data into a DataFrame
# data = pd.read_csv('your_data.csv')

# Filter data for the United States
mex_data = df_mental_health[df_mental_health['Entity'] == 'Mexico']

print(mex_data.head(2))

      Entity Code  Year  \
3630  Mexico  MEX  1990   
3631  Mexico  MEX  1991   

      Prevalence - Schizophrenia - Sex: Both - Age: Age-standardized (Percent)  \
3630                                           0.292561                          
3631                                           0.292781                          

      Prevalence - Bipolar disorder - Sex: Both - Age: Age-standardized (Percent)  \
3630                                           0.950546                             
3631                                           0.951822                             

      Prevalence - Eating disorders - Sex: Both - Age: Age-standardized (Percent)  \
3630                                           0.234103                             
3631                                           0.236205                             

      Prevalence - Anxiety disorders - Sex: Both - Age: Age-standardized (Percent)  \
3630                                           2.993664                              
3631                                           2.994335                              

      Prevalence - Drug use disorders - Sex: Both - Age: Age-standardized (Percent)  \
3630                                           0.520234                               
3631                                           0.521305                               

      Prevalence - Depressive disorders - Sex: Both - Age: Age-standardized (Percent)  \
3630                                           3.165633                                 
3631                                           3.185316                                 

      Prevalence - Alcohol use disorders - Sex: Both - Age: Age-standardized (Percent)  
3630                                           1.775613                                 
3631                                           1.730985                                 

disorder_columns = [
    'Prevalence - Schizophrenia - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Bipolar disorder - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Eating disorders - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Anxiety disorders - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Drug use disorders - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Depressive disorders - Sex: Both - Age: Age-standardized (Percent)',
    'Prevalence - Alcohol use disorders - Sex: Both - Age: Age-standardized (Percent)'
]


# Create a scatter plot with different colors for each disorder
plt.figure(figsize=(8, 6))
for column in disorder_columns:
    plt.scatter(mex_data['Year'], mex_data[column], label=column, alpha=0.7)

plt.xlabel('Year')
plt.ylabel('Prevalence (%)')
plt.title('Prevalence of Mental Health Disorders in the  Mexico')
plt.legend()
plt.grid(True)
plt.show()

As you can see the bipolar and eating disorders are at a similar rate and stable, similar to the U.S.. On the other hand We see a more drastic and uniform increase in Anxiety and Depressive disorders peaking around 2007/2008.

A Stunning Revelation¶

As I delved into the data, I stumbled upon a revelation that transcended mere statistics and charts. It was a connection between my friend's recollections and the dataset that brought me face to face with a significant historical event. The disturbing scenes he had witnessed during his time in Mexico, the clashes between cartels and the country's army – they all seemed to echo the tumultuous years of 2007 and 2008. Intrigued by the notion, I turned to the internet for confirmation and found a compelling correlation. According to sources such as Wikipedia and other reliable references, the timeframe he spoke of coincided with an intensified phase of the Mexican drug war, marked by a resolute government effort to combat the influence of cartels.

In this synergy between personal memory and data-driven insights, an unexpected truth emerged: the past can be unveiled in the patterns of data, an echo of historical narratives reverberating through the columns and rows of a spreadsheet.

I seemed to recall that the years he was referring to be similar to 2007/2008 and low and behold upon an internet search I discovered that according to Wikipedia (and others sources) https://en.wikipedia.org/wiki/Mexican_drug_war this time period was known as an escalation time period when the government really tried to earnestly battle the cartels.

So it seems that we detected a major world event indirectly by examining this dataset!

Linking to the American Fentanyl Crisis:¶

A deeper exploration into the web of connections within this dataset reveals an astonishing link to a crisis of global proportions – the American Fentanyl Crisis. The echoes of the Mexican drug war extend beyond borders, with repercussions reverberating in unexpected ways. The evidence is clear: witnesses testified that Transnational Criminal Organizations (TCOs) in Mexico have been smuggling lethal illicit fentanyl into the United States, bypassing the vigilance of Border Patrol agents and CBP Officers. Astonishingly, the cartels have extended their reach to employ Unmanned Aircraft Systems (UAS), commonly known as drones, to traffic fentanyl and other substances.

"In the hearing, witnesses confirmed that TCOs in Mexico are successfully smuggling mass quantities of deadly illicit fentanyl past Border Patrol agents and CBP Officers and into the United States. Not only are cartels smuggling on land, but they are now trafficking fentanyl and other drugs using Unmanned Aircraft Systems (UAS), or drones. Amid this administration’s historic border crisis and Secretary Mayorkas’ dereliction of duty, cartels have been able to reap billions of dollars in profits to increase their capabilities—leaving our nation’s dedicated Border Patrol agents at a disadvantage in the field."

Source: House Committee on Homeland Security%2C%20or%20drones.)

Against the backdrop of unprecedented border challenges and administrative complexities, the cartels have capitalized on their activities, amassing staggering profits that fuel their growing capabilities.

This revelation raises a haunting question: in a world interwoven by data and events, how many more stories lie hidden, waiting to be unveiled through the lens of information?

Conclusion and Future Inquiries¶

As we conclude this journey through the intricate tapestry of mental health data, historical echoes, and global crises, it's evident that data analysis goes beyond mere numbers and charts. It offers a unique lens through which we can peer into the complexities of our world, uncovering connections that might otherwise remain hidden. This exploration has not only provided insights into the prevalence of mental health disorders but has also highlighted the power of data in shedding light on broader societal dynamics.

We've traversed diverse regions and timeframes, witnessing the rise and fall of mental health trends, pondering the significance of columns like "Entity" and "Code," and raising questions about the prevalence rates across different age and gender groups. The convergence of personal recollections and historical events, as evidenced by the Mexican drug war, showcases how the past leaves its imprint on the data of the present.

Moreover, our journey led us to an unexpected connection – the ominous ties between the Mexican drug trade and the American Fentanyl Crisis. The dataset has unveiled the intricate ways in which data threads through global narratives, revealing the intricate interplay between international events, policy decisions, and societal well-being.

In closing, this endeavor has affirmed the notion that data is more than just information; it's a dynamic medium that bridges the gap between personal experiences and world events. As we move forward, let us remember that every cell in a spreadsheet holds the potential to unravel stories, challenge assumptions, and foster a deeper understanding of our world.

May this exploration be a reminder that the journey of data analysis is never truly finished; it's a continuous quest to unravel the mysteries that shape our lives, one dataset at a time.